Applying evolutionary algorithm to solve transportation problem

Name: Krittika Kantawong

Address: 333 Moo1, Thasud, Muang, Chiang Rai 57100

Organization : Mae Fah Luang University. School of Information Technology. Computer Engineering

Email : ask.library@mfu.ac.th

keyword: Evolutionary algorithm

LCSH: Evolutionary programming (Computer science)

Classification :.LCCS: QA76.618

LCSH: Evolutionary computation

LCSH: Genetic algorithms

Abstract: Transportation is a private and public organization that's involved in people’s lives. It is a major national expense investment and expense for governments that need to boost their nation's economy. Transportation’s cost resulted in an increase of price of goods for 70% and 10%-15% on household expenses and 4% in manufacturing cost. It was found that 76% of the products have been transferred from places to places by vehicle transportation which confirms the significance of transportation. It is clear that there are effective route planning benefits business and social opportunities. Therefore, this dissertation aims to design on improving an evolutionary algorithm to produce effective routes. Fuzzy technique was applied to deal with the uncertainty time windows constraint and produce the feasible population in the initialization stage. Then, the inter-route crossover, intra-route mutation, elitism strategy and onlooker bee probability selection method were enhanced in the original processes of evolutionary algorithm in order to improve the population. It was found that the proposed algorithm can provide proper initial solutions and an effective starting searching area. The computational results show that the proposed algorithm produces competitive results and is useful to serve customers who are distributed in random geographical area (problem class R), classified geographical area (problem class C) and mixed geographical area (problem class RC). In addition, the information regarding the p-value was resolved by the Wilcoxon signed-rank test for the considered testing instances that displays statistically superior performance at the 95% significance level (α = 0.05) on comparing algorithms. The computational results illustrate that the proposed algorithm produce satisfied results and can compete with other algorithms.

Mae Fah Luang University. The Learning Resources and Education Media Center

Address: Chiang Rai

Email: library@mfu.ac.th

Name: Roungsan Chaisricharoen

Role: Advisor

Email : ask.library@mfu.ac.th

Created: 2561

Modified: 2019-05-21

Issued: 2562

Issued: 2019-05-21

วิทยานิพนธ์/Thesis

application/pdf

CallNumber: Thesis QA76.618 K929a 2018

eng

DegreeName: Dotor of Philosophy

Level: Doctoral Degree

Descipline: Computer Engineering

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